In this paper, we propose PatchNet which learns discriminative features based on patches cropped from the entire face regions. Inspired by previous works [1]}, [2]}, [3]}, the patch-level inputs can enhance the data variation and enforce the network to learn spoof-specific features in the local region, and thus prevent the network from overfitting to the biases introduced by datasets. Instead of resizing the input face images into the same size as adopted by recent FAS works, we directly crop the fixed-size patches from raw facial captures to avoid the distortion of discriminative FAS cues. With the patch-level inputs, our PatchNet aims at classifying the corresponding fine-grained categories, i.e., the capturing devices and presenting materials, and we denote each category as a specific “patch-type". To enforce the network to learn robust spoof-related feature to recognize unseen patch types during testing, we adopt the angular margin-based softmax loss that is commonly used in face recognition tasks [4]}, [5]}, [6]}, which aims to optimize the face embedding on the normalized hypersphere (Figure REF ). Moreover, since the patch type classes are not symmetric between live and spoof faces, we propose “asymmetric angular margin loss” and impose a larger margin on live type classes. Inspired by the recent works on self-supervised learning [7]}, [8]}, [9]}, and the fact that material patterns are presented spatially in the entire face region, we also propose “self-supervised similarity loss” to regularize the features with location and rotation invariance.
